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Information Technology / Artificial intelligence

Machine learning

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Displaying 1 - 25 of 44

Quasi-Deterministic Channel Propagation Model for Human Sensing: Gesture Recognition Use Case

July 9, 2025
Author(s)
Jack Chuang, Raied Caromi, Jelena Senic, Samuel Berweger, Neeraj Varshney, Jian Wang, Anuraag Bodi, Camillo Gentile, Nada Golmie
We describe a quasi-determinstic channel propagation model for human gesture recognition reduced from real-time measurements with our context aware channel sounder, considering four human subjects and 20 distinct body motions, for a total of 120,000

Event Report for "MLXN25: Machine Learning for X-ray and Neutron Scattering"

July 3, 2025
Author(s)
Peter Beaucage, Tanny Andrea Chavez Esparza, Alexander Hexemer, Tyler Martin, Peter Muller-Buschbaum, Stephan Roth, Xiaoping Wang
The MLXN25 virtual event was held on April 15, 2025, as a continuous 24-hour global event, uniting over 300 registered participants from 18 countries and 20 user facilities to discuss how machine learning (ML) is transforming X-ray and neutron science

Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays

April 30, 2025
Author(s)
Anantha Rao, Donovan Buterakos, Barnaby van Straaten, Valentin John, Cecile Yu, Stefan Oosterhout, Lucas Stehouwer, Giordano Scappucci, Menno Veldhorst, Francesco Borsoi, Justyna Zwolak
Arrays of gate-defined semiconductor quantum dots are among the leading candidates for building scalable quantum processors. High-fidelity initialization, control, and readout of spin qubit registers require exquisite and targeted control over key

Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations

March 24, 2025
Author(s)
Apostol Vassilev, Alina Oprea, Alie Fordyce, Hyrum Anderson, Xander Davies, Maia Hamin
This NIST Trustworthy and Responsible AI report provides a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is arranged in a conceptual hierarchy that includes key types of ML methods, life cycle

Fire Data Generator (FD-Gen) v1.0.0

March 6, 2025
Author(s)
Hongqiang Fang, Wai Cheong Tam
This document serves as the documentation for the Fire Data Generator (FD-Gen), an automated tool designed to streamline the creation of multiple Fire Dynamics Simulator (FDS) input files. By employing Monte Carlo methods to sample relevant fire parameters

Autonomous bootstrapping of quantum dot devices

January 28, 2025
Author(s)
Anton Zubchenko, Danielle Middlebrooks, Torbjoern Rasmussen, Lara Lausen, Ferdinand Kuemmeth, Anasua Chatterjee, Justyna Zwolak
Semiconductor quantum dots (QDs) are a promising platform for multiple different qubit implementations, all of which are voltage controlled by programmable gate electrodes. However, as the QD arrays grow in size and complexity, tuning procedures that can

Automation of Quantum Dot Measurement Analysis via Explainable Machine Learning

January 13, 2025
Author(s)
Daniel Schug, Tyler Kovach, Michael Wolfe, Jared Benson, Sanghyeok Park, J. P. Dodson, Joelle Corrigan, Mark Eriksson, Justyna Zwolak
The rapid development of quantum dot (QD) devices for quantum computing has necessitated more efficient and automated methods for device characterization and tuning. Many of the measurements acquired during the tuning process come in the form of images

Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud

August 2, 2024
Author(s)
Anuraag Bodi, Raied Caromi, Jian Wang, Jelena Senic, Camillo Gentile, Hang Mi, Bo Ai, Ruisi He
Millimeter-wave (MmWave) channel characteristics are quite different from sub-6 GHz frequency bands. The major differences include higher path loss and sparser multipath components (MPCs), resulting in more significant time-varying characteristics in

Human-in-the-loop Technical Document Annotation: Developing and Validating a System to Provide Machine-Assistance for Domain-Specific Text Analysis

May 14, 2024
Author(s)
Juan Fung, Zongxia Li, Daniel Stephens, Andrew Mao, Pranav Goel, Emily Walpole, Alden A. Dima, Jordan Boyd-Graber
In this report, we address the following question: to what extent can machine learning assist a human with traditional text analysis, such as content analysis or grounded theory in the social sciences? In practice, such tasks require humans to review and

Measurement-driven neural-network training for integrated magnetic tunnel junction arrays

May 14, 2024
Author(s)
William Borders, Advait Madhavan, Matthew Daniels, Vasileia Georgiou, Martin Lueker-Boden, Tiffany Santos, Patrick Braganca, Mark Stiles, Jabez J. McClelland, Brian Hoskins
The increasing scale of neural networks needed to support more complex applications has led to an increasing requirement for area- and energy-efficient hardware. One route to meeting the budget for these applications is to circumvent the von Neumann

AI-Based Environment Segmentation Using a Context-Aware Channel Sounder

April 26, 2024
Author(s)
Anuraag Bodi, Samuel Berweger, Raied Caromi, Jihoon Bang, Jelena Senic, Camillo Gentile
We describe how the data acquired from the camera and Lidar systems of our context-aware radio-frequency (RF) channel sounder is used to reconstruct a 3D mesh of the surrounding environment, segmented and classified into discrete objects. First, the images

Context-Aware Channel Sounder for AI-Assisted Radio-Frequency Channel Modeling

April 26, 2024
Author(s)
Camillo Gentile, Jelena Senic, Anuraag Bodi, Samuel Berweger, Raied Caromi, Nada Golmie
We describe a context-aware channel sounder that consists of three separate systems: a radio-frequency system to extract multipaths scattered from the surrounding environment in the 3D geometrical domain, a Lidar system to generate a point cloud of the

An Introduction to Machine Learning Lifecycle Ontology and its Applications

April 19, 2024
Author(s)
Milos Drobnjakovic, Perawit Charoenwut, Ana Nikolov, Hakju Oh, Boonserm Kulvatunyou
Machine Learning (ML) adoption is on the rapid rise, with a nearly 40% compound annual growth rate over the next decade. In other words, companies will be flooded with ML models developed with different datasets and software. The ability to have

2024 NIST Generative AI (GenAI): Data Creation Specification for Text-to-Text (T2T) Generators

April 1, 2024
Author(s)
Yooyoung Lee, George Awad, Asad Butt, Lukas Diduch, Kay Peterson, Seungmin Seo, Ian Soboroff, Hariharan Iyer
Generator (G) teams will be tested on their system ability to generate content that is indistinguishable from human-generated content. For the pilot study, the evaluation will help determine strengths and weaknesses in their approaches including insights

2024 NIST Generative AI (GenAI): Evaluation Plan for Text-to-Text (T2T) Discriminators

April 1, 2024
Author(s)
Yooyoung Lee, George Awad, Asad Butt, Lukas Diduch, Kay Peterson, Seungmin Seo, Ian Soboroff, Hariharan Iyer
Generator (G) teams will be tested on their system's ability to generate content that is indistinguishable from human-generated content. For the pilot study, the evaluation will help determine strengths and weaknesses in their approaches including insights

Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis

March 23, 2024
Author(s)
Zongxia Li, Andrew Mao, Jordan Boyd-Graber, Daniel Stephens, Emily Walpole, Alden A. Dima, Juan Fung
Topic models are a popular tool for understanding text collections, but their evaluation has been a point of contention. Automated evaluation metrics such as coherence are often used, however, their validity has been questioned for neural topic models

Explainable Classification Techniques for Quantum Dot Device Measurements

March 12, 2024
Author(s)
Daniel Schug, Tyler Kovach, Jared Benson, Mark Eriksson, Justyna Zwolak
In the physical sciences, there is an increased need for robust feature representations of image data: image acquisition, in the generalized sense of two-dimensional data, is now widespread across a large number of fields, including quantum information

Building Fire Hazard Predictions Using Machine Learning

January 26, 2024
Author(s)
Eugene Yujun Fu, Wai Cheong Tam, Tianhang Zhang, Xinyan Huang
The lack of information on the fire ground has always been the leading factor in making wrong decisions . Wrong decisions can be made by individual firefighters, their local chiefs, and/or the incident commander. Any wrong decision at any level (scale)
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